Databricks-Machine-Learning-Professional - Databricks Certified Machine Learning Professional

The log_metric function is used to log a numeric metric value to the current MLflow run. It takes the name of the metric and the value as arguments. In this case, the name of the metric is “rmse” and the value is the variable rmse. The log_metric function can be used multiple times to log different metrics or update the same metric. The log_artifact function is used to log a file or directory as an artifact to the current MLflow run. The log_model function is used to log a model with a specified flavor to the current MLflow run. The log_param function is used to log a key-value pair as a parameter to the current MLflow run. There is a way to store values like this using the log_metric function, so option E is incorrect.https://docs.databricks.com/

The MLflow Model Registry provides a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage, model versioning, model aliasing, model tagging, and annotations. The Model Registry defines four model stages that represent the lifecycle of a model: None, Staging, Production, and Archived. Each model stage has a different meaning and purpose:

• None: This is the default stage when a new model version is registered. It indicates that the model version is not ready for any downstream use cases and needs further development or testing.
• Staging: This stage indicates that the model version is ready for testing or validation in a pre-production environment. It can be used for quality assurance, user acceptance testing, or performance evaluation before moving to production.
• Production: This stage indicates that the model version is ready for deployment in a production environment. It can be used for serving predictions, scoring data, or supporting business decisions. Only one model version per registered model can be in the production stage at a time.
• Archived: This stage indicates that the model version is no longer in active use and can be safely deleted or moved to a long-term storage. It can be used for historical or audit purposes, or for restoring a previous model version if needed.

References:

• MLflow Model Registry Documentation, p. 1-2
• Introducing the MLflow Model Registry - Databricks, p. 1-2
• Manage MLflow Models — Databricks Documentation, p. 1-2

 The mlflow.register_model function takes two arguments: model_uri and name. The model_uri is the URI of the model that was logged to MLflow, which can be obtained from the best run object. The name is the name of the registered model in the MLflow Model Registry. Therefore, the correct line of code to register the model is:

mlflow.register_model(model_uri, “best_model”)

This will create a new registered model with the name “best_model” and register the model version from the best run as the first version of that model.

References:

• [mlflow.register_model — MLflow 1.22.0 documentation]
• [MLflow Model Registry — Databricks Documentation]
• [Manage MLflow Models — Databricks Documentation] Message has links.